A deep learning based approach for analog hardware implementation of delayed feedback reservoir computing system

As the 2020 roadblock approaches, the need of breakthrough in computing systems has directed researchers to novel computing paradigms. The recently emerged reservoir computing model, delayed feedback reservoir (DFR) computing, only utilizes one nonlinear neuron along with a delay loop. It not only offers the ease of hardware implementation but also enables the optimal performance contributed by the inherent delay and its rich intrinsic dynamics. The field of deep learning has attracted worldwide attention due to its hierarchical architecture that allows more efficient performance than a shallow structure. Along with our analog hardware implementation of the DFR, we investigate the possibility of merging deep learning and DFR computing systems. By evaluating the results, deep DFR models demonstrate 50%–81% better performance during training and 39%–64% performance improvement during testing than shallow leaky echo state network (ESN) model. Due to the difference in architecture, the training time of MI (multiple inputs)-deep DFR requires approximately 21% longer than that of the deep DFR model. Our approach offers the great potential and promise in the realization of analog hardware implementations for deep DFR systems.